The World Wide Web generates more and more data with links and node contents, which are always modeled as attributed networks. The identification of network communities plays an important role for people to understand and utilize the semantic functions of the data. A few methods based on non-negative matrix factorization (NMF) have been proposed to detect community structure with semantic information in attributed networks. However, previous methods have not modeled some key factors (which affect the link generating process together), including prior information, the heterogeneity of node degree, as well as the interactions among communities. The three factors have been demonstrated to primarily affect the results. In this paper, we propose a semi-supervised community detection method on attributed networks by simultaneously considering these three factors. First, a semi-supervised non-negative matrix tri-factorization model with node popularity (i.e., PSSNMTF) is designed to detect communities on the topology of the network. And then node contents are integrated into the PSSNMTF model to find the semantic communities more accurately, namely PSSNMTFC. Parameters of the PSSNMTFC model is estimated by using the gradient descent method. Experiments on some real and artificial networks illustrate that our new method is superior over some related state-of-the-art methods in terms of accuracy.

万维网通过链接和节点内容生成越来越多的数据，这些数据始终被建模为属性网络。网络社区的识别对于人们理解和利用数据的语义功能起着重要的作用。提出了几种基于非负矩阵分解的方法来检测属性网络中带有语义信息的社区结构。但是，以前的方法没有对一些关键因素（它们一起影响链接生成过程）进行建模，包括先验信息，节点程度的异质性以及社区之间的相互作用。已经证明这三个因素主要影响结果。在本文中，通过同时考虑这三个因素，我们提出了一种属性网络上的半监督社区检测方法。首先，设计一种具有节点流行度的半监督非负矩阵三因子分解模型（即PSSNMTF），以检测网络拓扑上的社区。然后将节点内容集成到PSSNMTF模型中，以更准确地找到语义社区，即PSSNMTFC。PSSNMTFC模型的参数通过使用梯度下降法进行估算。在一些真实的和人工的网络上进行的实验表明，我们的新方法在准确性方面优于一些相关的最新技术。然后将节点内容集成到PSSNMTF模型中，以更准确地找到语义社区，即PSSNMTFC。PSSNMTFC模型的参数通过使用梯度下降法进行估算。在一些真实的和人工的网络上进行的实验表明，我们的新方法在准确性方面优于一些相关的最新技术。然后将节点内容集成到PSSNMTF模型中，以更准确地找到语义社区，即PSSNMTFC。PSSNMTFC模型的参数通过使用梯度下降法进行估算。在一些真实的和人工的网络上进行的实验表明，我们的新方法在准确性方面优于一些相关的最新技术。